{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "pdf-title"
    ]
   },
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "  print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.2121476417505994e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "pdf-ignore-input"
    ]
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from imageio import imread\n",
    "from PIL import Image\n",
    "\n",
    "kitten = imread('notebook_images/kitten.jpg')\n",
    "puppy = imread('notebook_images/puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "resized_puppy = np.array(Image.fromarray(puppy).resize((img_size, img_size)))\n",
    "resized_kitten = np.array(Image.fromarray(kitten_cropped).resize((img_size, img_size)))\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = resized_puppy.transpose((2, 0, 1))\n",
    "x[1, :, :, :] = resized_kitten.transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_no_ax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_no_ax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_no_ax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_no_ax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_no_ax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_no_ax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_no_ax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.159803161159293e-08\n",
      "dw error:  2.2471264748452487e-10\n",
      "db error:  3.37264006649648e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around e-8 or less.\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.1666665157267834e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be on the order of e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Max-Pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223145e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be on the order of e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 5.148098s\n",
      "Fast: 0.020025s\n",
      "Speedup: 257.077221x\n",
      "Difference:  4.926407851494105e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 8.180718s\n",
      "Fast: 0.019898s\n",
      "Speedup: 411.124095x\n",
      "dx difference:  1.949764775345631e-11\n",
      "dw difference:  3.681156828004736e-13\n",
      "db difference:  3.481354613192702e-14\n"
     ]
    }
   ],
   "source": [
    "# Rel errors should be around e-9 or less\n",
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.350887s\n",
      "fast: 0.002023s\n",
      "speedup: 173.470769x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 1.049588s\n",
      "fast: 0.010801s\n",
      "speedup: 97.178649x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "# Relative errors should be close to 0.0\n",
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks. Run the cells below to sanity check they're working."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  9.591132621921372e-09\n",
      "dw error:  5.802391137330214e-09\n",
      "db error:  1.0146343411762047e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  1.5218619980349303e-09\n",
      "dw error:  2.702022646099404e-10\n",
      "db error:  1.451272393591721e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "# Relative errors should be around e-8 or less\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Remember you can use the fast/sandwich layers (already imported for you) in your implementation. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization the loss should go up slightly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "log(C): 2.302585092994046\n",
      "Initial loss (no regularization):  2.302586071243987\n",
      "Initial loss (with regularization):  2.508255638232932\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "print('log(C):', np.log(10))\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to the order of e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 3.053965e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.422399e-04\n",
      "b1 max relative error: 3.397321e-06\n",
      "b2 max relative error: 2.517459e-03\n",
      "b3 max relative error: 9.711800e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "# Errors should be small, but correct implementations may have\n",
    "# relative errors up to the order of e-2\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.200000; val_acc: 0.137000\n",
      "(Iteration 2 / 30) loss: 3.102925\n",
      "(Epoch 1 / 15) train acc: 0.140000; val_acc: 0.087000\n",
      "(Iteration 3 / 30) loss: 2.270330\n",
      "(Iteration 4 / 30) loss: 2.096705\n",
      "(Epoch 2 / 15) train acc: 0.240000; val_acc: 0.094000\n",
      "(Iteration 5 / 30) loss: 1.838880\n",
      "(Iteration 6 / 30) loss: 1.934188\n",
      "(Epoch 3 / 15) train acc: 0.510000; val_acc: 0.173000\n",
      "(Iteration 7 / 30) loss: 1.827912\n",
      "(Iteration 8 / 30) loss: 1.639574\n",
      "(Epoch 4 / 15) train acc: 0.520000; val_acc: 0.188000\n",
      "(Iteration 9 / 30) loss: 1.330082\n",
      "(Iteration 10 / 30) loss: 1.756115\n",
      "(Epoch 5 / 15) train acc: 0.630000; val_acc: 0.167000\n",
      "(Iteration 11 / 30) loss: 1.024162\n",
      "(Iteration 12 / 30) loss: 1.041826\n",
      "(Epoch 6 / 15) train acc: 0.750000; val_acc: 0.229000\n",
      "(Iteration 13 / 30) loss: 1.142777\n",
      "(Iteration 14 / 30) loss: 0.835706\n",
      "(Epoch 7 / 15) train acc: 0.790000; val_acc: 0.247000\n",
      "(Iteration 15 / 30) loss: 0.587786\n",
      "(Iteration 16 / 30) loss: 0.645509\n",
      "(Epoch 8 / 15) train acc: 0.820000; val_acc: 0.252000\n",
      "(Iteration 17 / 30) loss: 0.786844\n",
      "(Iteration 18 / 30) loss: 0.467054\n",
      "(Epoch 9 / 15) train acc: 0.820000; val_acc: 0.178000\n",
      "(Iteration 19 / 30) loss: 0.429880\n",
      "(Iteration 20 / 30) loss: 0.635498\n",
      "(Epoch 10 / 15) train acc: 0.900000; val_acc: 0.206000\n",
      "(Iteration 21 / 30) loss: 0.365807\n",
      "(Iteration 22 / 30) loss: 0.284220\n",
      "(Epoch 11 / 15) train acc: 0.820000; val_acc: 0.201000\n",
      "(Iteration 23 / 30) loss: 0.469343\n",
      "(Iteration 24 / 30) loss: 0.509369\n",
      "(Epoch 12 / 15) train acc: 0.920000; val_acc: 0.211000\n",
      "(Iteration 25 / 30) loss: 0.111638\n",
      "(Iteration 26 / 30) loss: 0.145388\n",
      "(Epoch 13 / 15) train acc: 0.930000; val_acc: 0.213000\n",
      "(Iteration 27 / 30) loss: 0.155575\n",
      "(Iteration 28 / 30) loss: 0.143398\n",
      "(Epoch 14 / 15) train acc: 0.960000; val_acc: 0.212000\n",
      "(Iteration 29 / 30) loss: 0.158160\n",
      "(Iteration 30 / 30) loss: 0.118934\n",
      "(Epoch 15 / 15) train acc: 0.990000; val_acc: 0.220000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "# print(model.get_shape())\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=15, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304740\n",
      "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
      "(Iteration 21 / 980) loss: 2.277595\n",
      "(Iteration 41 / 980) loss: 2.105168\n",
      "(Iteration 61 / 980) loss: 1.955614\n",
      "(Iteration 81 / 980) loss: 1.901870\n",
      "(Iteration 101 / 980) loss: 1.786646\n",
      "(Iteration 121 / 980) loss: 1.607626\n",
      "(Iteration 141 / 980) loss: 1.901893\n",
      "(Iteration 161 / 980) loss: 1.900993\n",
      "(Iteration 181 / 980) loss: 1.974500\n",
      "(Iteration 201 / 980) loss: 1.930574\n",
      "(Iteration 221 / 980) loss: 2.061199\n",
      "(Iteration 241 / 980) loss: 1.680599\n",
      "(Iteration 261 / 980) loss: 1.758424\n",
      "(Iteration 281 / 980) loss: 1.987053\n",
      "(Iteration 301 / 980) loss: 1.756236\n",
      "(Iteration 321 / 980) loss: 1.879092\n",
      "(Iteration 341 / 980) loss: 1.813254\n",
      "(Iteration 361 / 980) loss: 2.129946\n",
      "(Iteration 381 / 980) loss: 1.438068\n",
      "(Iteration 401 / 980) loss: 1.829907\n",
      "(Iteration 421 / 980) loss: 1.524082\n",
      "(Iteration 441 / 980) loss: 1.729550\n",
      "(Iteration 461 / 980) loss: 1.840234\n",
      "(Iteration 481 / 980) loss: 1.830851\n",
      "(Iteration 501 / 980) loss: 1.595853\n",
      "(Iteration 521 / 980) loss: 2.126786\n",
      "(Iteration 541 / 980) loss: 1.692218\n",
      "(Iteration 561 / 980) loss: 1.839294\n",
      "(Iteration 581 / 980) loss: 1.495706\n",
      "(Iteration 601 / 980) loss: 1.655335\n",
      "(Iteration 621 / 980) loss: 1.683438\n",
      "(Iteration 641 / 980) loss: 1.742117\n",
      "(Iteration 661 / 980) loss: 1.828452\n",
      "(Iteration 681 / 980) loss: 1.860717\n",
      "(Iteration 701 / 980) loss: 1.546998\n",
      "(Iteration 721 / 980) loss: 1.541053\n",
      "(Iteration 741 / 980) loss: 1.745391\n",
      "(Iteration 761 / 980) loss: 1.678145\n",
      "(Iteration 781 / 980) loss: 2.111524\n",
      "(Iteration 801 / 980) loss: 1.900898\n",
      "(Iteration 821 / 980) loss: 1.668368\n",
      "(Iteration 841 / 980) loss: 1.589296\n",
      "(Iteration 861 / 980) loss: 1.778009\n",
      "(Iteration 881 / 980) loss: 1.700346\n",
      "(Iteration 901 / 980) loss: 1.685917\n",
      "(Iteration 921 / 980) loss: 1.775121\n",
      "(Iteration 941 / 980) loss: 1.903952\n",
      "(Iteration 961 / 980) loss: 1.671379\n",
      "(Epoch 1 / 1) train acc: 0.446000; val_acc: 0.464000\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. As proposed in the original paper (link in `BatchNormalization.ipynb`), batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect every feature channel's statistics e.g. mean, variance to be relatively consistent both between different images, and different locations within the same image -- after all, every feature channel is produced by the same convolutional filter! Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over the minibatch dimension `N` as well the spatial dimensions `H` and `W`.\n",
    "\n",
    "\n",
    "[1] [Sergey Ioffe and Christian Szegedy, \"Batch Normalization: Accelerating Deep Network Training by Reducing\n",
    "Internal Covariate Shift\", ICML 2015.](https://arxiv.org/abs/1502.03167)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [9.33463814 8.90909116 9.11056338]\n",
      "  Stds:  [3.61447857 3.19347686 3.5168142 ]\n",
      "After spatial batch normalization:\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [ 6.18949336e-16  5.99520433e-16 -1.22124533e-16]\n",
      "  Stds:  [0.99999962 0.99999951 0.9999996 ]\n",
      "After spatial batch normalization (nontrivial gamma, beta):\n",
      "  Shape:  (2, 3, 4, 5)\n",
      "  Means:  [6. 7. 8.]\n",
      "  Stds:  [2.99999885 3.99999804 4.99999798]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "After spatial batch normalization (test-time):\n",
      "  means:  [-0.08034406  0.07562881  0.05716371  0.04378383]\n",
      "  stds:  [0.96718744 1.0299714  1.02887624 1.00585577]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  2.786648201640115e-07\n",
      "dgamma error:  7.0974817113608705e-12\n",
      "dbeta error:  3.275608725278405e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "#You should expect errors of magnitudes between 1e-12~1e-06\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Group Normalization\n",
    "In the previous notebook, we mentioned that Layer Normalization is an alternative normalization technique that mitigates the batch size limitations of Batch Normalization. However, as the authors of [2] observed, Layer Normalization does not perform as well as Batch Normalization when used with Convolutional Layers:\n",
    "\n",
    ">With fully connected layers, all the hidden units in a layer tend to make similar contributions to the final prediction, and re-centering and rescaling the summed inputs to a layer works well. However, the assumption of similar contributions is no longer true for convolutional neural networks. The large number of the hidden units whose\n",
    "receptive fields lie near the boundary of the image are rarely turned on and thus have very different\n",
    "statistics from the rest of the hidden units within the same layer.\n",
    "\n",
    "The authors of [3] propose an intermediary technique. In contrast to Layer Normalization, where you normalize over the entire feature per-datapoint, they suggest a consistent splitting of each per-datapoint feature into G groups, and a per-group per-datapoint normalization instead. \n",
    "\n",
    "![Comparison of normalization techniques discussed so far](notebook_images/normalization.png)\n",
    "<center>**Visual comparison of the normalization techniques discussed so far (image edited from [3])**</center>\n",
    "\n",
    "Even though an assumption of equal contribution is still being made within each group, the authors hypothesize that this is not as problematic, as innate grouping arises within features for visual recognition. One example they use to illustrate this is that many high-performance handcrafted features in traditional Computer Vision have terms that are explicitly grouped together. Take for example Histogram of Oriented Gradients [4]-- after computing histograms per spatially local block, each per-block histogram is normalized before being concatenated together to form the final feature vector.\n",
    "\n",
    "You will now implement Group Normalization. Note that this normalization technique that you are to implement in the following cells was introduced and published to ECCV just in 2018 -- this truly is still an ongoing and excitingly active field of research!\n",
    "\n",
    "[2] [Ba, Jimmy Lei, Jamie Ryan Kiros, and Geoffrey E. Hinton. \"Layer Normalization.\" stat 1050 (2016): 21.](https://arxiv.org/pdf/1607.06450.pdf)\n",
    "\n",
    "\n",
    "[3] [Wu, Yuxin, and Kaiming He. \"Group Normalization.\" arXiv preprint arXiv:1803.08494 (2018).](https://arxiv.org/abs/1803.08494)\n",
    "\n",
    "\n",
    "[4] [N. Dalal and B. Triggs. Histograms of oriented gradients for\n",
    "human detection. In Computer Vision and Pattern Recognition\n",
    "(CVPR), 2005.](https://ieeexplore.ieee.org/abstract/document/1467360/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for group normalization in the function `spatial_groupnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before spatial group normalization:\n",
      "  Shape:  (2, 6, 4, 5)\n",
      "  Means:  [9.72505327 8.51114185 8.9147544  9.43448077]\n",
      "  Stds:  [3.67070958 3.09892597 4.27043622 3.97521327]\n",
      "After spatial group normalization:\n",
      "  Shape:  (2, 6, 4, 5)\n",
      "  Means:  [-7.40148683e-18  1.11022302e-17  1.23049719e-16  1.29526020e-17]\n",
      "  Stds:  [0.99999834 0.99999782 0.99999873 0.99999691]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "x_g = x.reshape((N*G,-1))\n",
    "print('Before spatial group normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x_g.mean(axis=1))\n",
    "print('  Stds: ', x_g.std(axis=1))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones((1,C,1,1)), np.zeros((1,C,1,1))\n",
    "bn_param = {'mode': 'train'}\n",
    "\n",
    "out, _ = spatial_groupnorm_forward(x, gamma, beta, G, bn_param)\n",
    "out_g = out.reshape((N*G,-1))\n",
    "print('After spatial group normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out_g.mean(axis=1))\n",
    "print('  Stds: ', out_g.std(axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial group normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_groupnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx error:  5.069505905384412e-08\n",
      "dgamma error:  3.891606265500656e-12\n",
      "dbeta error:  1.3112820990521168e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 6, 4, 5\n",
    "G = 2\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(1,C,1,1)\n",
    "beta = np.random.randn(1,C,1,1)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "gn_param = {}\n",
    "fx = lambda x: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fg = lambda a: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "fb = lambda b: spatial_groupnorm_forward(x, gamma, beta, G, gn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_groupnorm_forward(x, gamma, beta, G, gn_param)\n",
    "dx, dgamma, dbeta = spatial_groupnorm_backward(dout, cache)\n",
    "#You should expect errors of magnitudes between 1e-12~1e-07\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  }
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